To show that our representation matches the original one generated by the LLM,
we first show that learning using the original embedding dimensions as features and learning using the conceptual features yield similar classifiers.
Most works try to show such similarity by comparing accuracy results.
This method, however is prone to errors. Two classifiers might
give us accuracy of 80%, while agreeing only on 60% of the cases.
Instead, we use a method that is used for rater agreement, reporting
two numbers: the raw agreement and the Cohen's kappa coefficient [1]}.
